BIOMASSA <- read.csv("biodepth/BiodepthSpeciesBiomass.csv", sep = ";", h=T)
Sweden e Sheffield não tem funções de solo medidas & só tem funcoes medidas pra o ano 3 + portugal só sobra 2 amostras quando tira as especies que nao tenho traits = vou filtrar esses
BIOMASSA <- BIOMASSA %>%
filter(!(site %in% c("Sweden", "Sheffield", "Portugal"))) %>%
filter(year == 3)
###Biomassa relativa
BIOMASSA <- BIOMASSA %>%
group_by(year, site, block, plot) %>%
mutate(percentage_biomass = 100 * biomass / sum(biomass, na.rm = TRUE))
datatable(BIOMASSA, options= list(deferRender = TRUE, scrollY = "350px", scrollX=T, scroller = TRUE, autoWidth=T), rownames=T)
ggplot(BIOMASSA, aes(x = biomass)) +
geom_histogram(binwidth = 10, fill = "lightblue", color = "black") +
labs(title = "Biomassa Biodepth",
x = "Biomassa",
y = "Frequência") +
theme_minimal()
ggplot(BIOMASSA, aes(x = percentage_biomass)) +
geom_histogram(binwidth = 10, fill = "lightblue", color = "black") +
labs(title = "Biomassa relativa Biodepth",
x = "Biomassa relativa",
y = "Frequência") +
theme_minimal()
Passando de biomassa para composição de espécies
COMPOSICAO <- BIOMASSA %>%
group_by(year,location,site,block,plot,sr,fr,fgc,mix.nest,mix,grass,forb,legume,funct,GRASS,FORB,LEG) %>%
pivot_wider(
names_from = species,
values_from = percentage_biomass,
values_fill = list(percentage_biomass=0))
#Germany
COMPOSICAO_GERMANY <- COMPOSICAO %>%
filter(site == "Germany")
#matrix de abundancia germany
abundance_germany <- COMPOSICAO_GERMANY %>%
ungroup() %>%
mutate(unique_plot = paste(plot, block, year, site, sep = "_")) %>%
group_by(unique_plot) %>%
summarise(across(where(is.numeric), sum, na.rm = TRUE)) %>%
column_to_rownames(var = "unique_plot") %>%
dplyr::select(-year,-location, -sr, -fr,-fgc, -mix.nest, -mix, -biomass)
## Warning: There was 1 warning in `summarise()`.
## ℹ In argument: `across(where(is.numeric), sum, na.rm = TRUE)`.
## ℹ In group 1: `unique_plot = "B1P001_A_3_Germany"`.
## Caused by warning:
## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
## Supply arguments directly to `.fns` through an anonymous function instead.
##
## # Previously
## across(a:b, mean, na.rm = TRUE)
##
## # Now
## across(a:b, \(x) mean(x, na.rm = TRUE))
# Cria a coluna 'riqueza' contando o número de espécies em cada plot
abundance_germany$riqueza <- rowSums(abundance_germany > 0)
abundance_germany_filtrado <- abundance_germany[abundance_germany$riqueza > 3, ]
abundance_germany_filtrado$riqueza <- NULL
#Remover espécies que não aparecem em nenhum plot
abundance_germany_filtrado <- abundance_germany_filtrado[, colSums(abundance_germany_filtrado, na.rm = TRUE) > 0]
abundance_matrix_germany <- as.matrix(abundance_germany_filtrado)
#Funcional Germany
# Funcional Germany
SPECIES_germany <- data.frame(species = colnames(abundance_matrix_germany))
SPECIES_germany$species <- tolower(SPECIES_germany$species)
TRAITS$species <- tolower(TRAITS$species)
TRAITS_germany <- SPECIES_germany %>%
inner_join(TRAITS, by = "species")
missing_traits_species_germany <- SPECIES_germany %>%
anti_join(TRAITS, by = "species")
# Diversidade funcional Germany
rownames(TRAITS_germany) <- TRAITS_germany$species
TRAITS_rn_germany <- TRAITS_germany[,-1]
TRAITS_rn_germany <- TRAITS_rn_germany[order(rownames(TRAITS_rn_germany)), ]
# Matriz de distância dos traits
euclid_dis_germany <- vegdist(TRAITS_rn_germany, "euclidean")
trait_dat_germany <- as.matrix(euclid_dis_germany)
colnames(abundance_matrix_germany) <- tolower(trimws(colnames(abundance_matrix_germany)))
abundance_matrix_germany <- abundance_matrix_germany[, order(colnames(abundance_matrix_germany))]
fd <- dbFD(trait_dat_germany, abundance_matrix_germany)
## FRic: Dimensionality reduction was required. The last 23 PCoA axes (out of 26 in total) were removed.
## FRic: Quality of the reduced-space representation = 0.9860835
Functional_germany <- data.frame(raoq = fd$RaoQ, FRic = fd$FRic,FEve = fd$FEve, FDis = fd$FDis, FD = fd$nbsp )
datatable(Functional_germany, options= list(deferRender = TRUE, scroller = TRUE, scrollX=T, scrollY = "350px", autoWidth=T), rownames=T)
hist(Functional_germany$FRic)
hist(Functional_germany$raoq)
#Greece
COMPOSICAO_GREECE <- COMPOSICAO %>%
filter(site == "Greece")
#matrix de abundancia greece
abundance_greece <- COMPOSICAO_GREECE %>%
ungroup() %>%
mutate(unique_plot = paste(plot, block, year, site, sep = "_")) %>%
group_by(unique_plot) %>%
summarise(across(where(is.numeric), sum, na.rm = TRUE)) %>%
column_to_rownames(var = "unique_plot") %>%
dplyr::select(-year,-location, -sr, -fr,-fgc, -mix.nest, -mix, -biomass)
# Cria a coluna 'riqueza' contando o número de espécies em cada plot
abundance_greece$riqueza <- rowSums(abundance_greece > 0)
abundance_greece_filtrado <- abundance_greece[abundance_greece$riqueza > 3, ]
abundance_greece_filtrado$riqueza <- NULL
#Remover espécies que não aparecem em nenhum plot
abundance_greece_filtrado <- abundance_greece_filtrado[, colSums(abundance_greece_filtrado, na.rm = TRUE) > 0]
abundance_matrix_greece <- as.matrix(abundance_greece_filtrado)
#Funcional Greece
SPECIES_greece <- data.frame(species = colnames(abundance_matrix_greece))
SPECIES_greece$species <- tolower(SPECIES_greece$species)
TRAITS$species <- tolower(TRAITS$species)
TRAITS_greece <- SPECIES_greece %>%
inner_join(TRAITS, by = "species")
missing_traits_species_greece <- SPECIES_greece %>%
anti_join(TRAITS, by = "species")
# Diversidade funcional Greece
rownames(TRAITS_greece) <- TRAITS_greece$species
TRAITS_rn_greece <- TRAITS_greece[,-1]
TRAITS_rn_greece$SLA <- log(TRAITS_rn_greece$SLA)
TRAITS_rn_greece$height <- log(TRAITS_rn_greece$height)
TRAITS_rn_greece <- TRAITS_rn_greece[order(rownames(TRAITS_rn_greece)), ]
euclid_dis_greece <- vegdist(TRAITS_rn_greece, "euclidean")
trait_dat_greece <- as.matrix(euclid_dis_greece)
colnames(abundance_matrix_greece) <- tolower(trimws(colnames(abundance_matrix_greece)))
abundance_matrix_greece <- abundance_matrix_greece[, order(colnames(abundance_matrix_greece))]
fd_greece <- dbFD(trait_dat_greece, abundance_matrix_greece)
## FRic: Dimensionality reduction was required. The last 7 PCoA axes (out of 10 in total) were removed.
## FRic: Quality of the reduced-space representation = 0.9516844
Functional_greece <- data.frame(raoq = fd_greece$RaoQ, FRic = fd_greece$FRic, FEve = fd_greece$FEve, FDis = fd_greece$FDis, FD = fd_greece$nbsp)
datatable(Functional_greece, options= list(deferRender = TRUE, scroller = TRUE, scrollX = T, scrollY = "350px", autoWidth = T), rownames = T)
hist(Functional_greece$FRic)
hist(Functional_greece$raoq)
#Ireland
COMPOSICAO_IRELAND <- COMPOSICAO %>%
filter(site == "Ireland")
#matrix de abundancia ireland
abundance_ireland <- COMPOSICAO_IRELAND %>%
ungroup() %>%
mutate(unique_plot = paste(plot, block, year, site, sep = "_")) %>%
group_by(unique_plot) %>%
summarise(across(where(is.numeric), sum, na.rm = TRUE)) %>%
column_to_rownames(var = "unique_plot") %>%
dplyr::select(-year,-location, -sr, -fr,-fgc, -mix.nest, -mix, -biomass)
# Cria a coluna 'riqueza' contando o número de espécies em cada plot
abundance_ireland$riqueza <- rowSums(abundance_ireland > 0)
abundance_ireland_filtrado <- abundance_ireland[abundance_ireland$riqueza > 3, ]
abundance_ireland_filtrado$riqueza <- NULL
#Remover espécies que não aparecem em nenhum plot
abundance_ireland_filtrado <- abundance_ireland_filtrado[, colSums(abundance_ireland_filtrado, na.rm = TRUE) > 0]
abundance_matrix_ireland <- as.matrix(abundance_ireland_filtrado)
#Funcional Ireland
SPECIES_ireland <- data.frame(species = colnames(abundance_matrix_ireland))
SPECIES_ireland$species <- tolower(SPECIES_ireland$species)
TRAITS$species <- tolower(TRAITS$species)
TRAITS_ireland <- SPECIES_ireland %>%
inner_join(TRAITS, by = "species")
missing_traits_species_ireland <- SPECIES_ireland %>%
anti_join(TRAITS, by = "species")
# Diversidade funcional Ireland
rownames(TRAITS_ireland) <- TRAITS_ireland$species
TRAITS_rn_ireland <- TRAITS_ireland[,-1]
TRAITS_rn_ireland$SLA <- log(TRAITS_rn_ireland$SLA)
TRAITS_rn_ireland$height <- log(TRAITS_rn_ireland$height)
TRAITS_rn_ireland <- TRAITS_rn_ireland[order(rownames(TRAITS_rn_ireland)), ]
euclid_dis_ireland <- vegdist(TRAITS_rn_ireland, "euclidean")
trait_dat_ireland <- as.matrix(euclid_dis_ireland)
colnames(abundance_matrix_ireland) <- tolower(trimws(colnames(abundance_matrix_ireland)))
abundance_matrix_ireland <- abundance_matrix_ireland[, order(colnames(abundance_matrix_ireland))]
fd_ireland <- dbFD(trait_dat_ireland, abundance_matrix_ireland)
## FRic: Dimensionality reduction was required. The last 7 PCoA axes (out of 10 in total) were removed.
## FRic: Quality of the reduced-space representation = 0.9628097
Functional_ireland <- data.frame(raoq = fd_ireland$RaoQ, FRic = fd_ireland$FRic, FEve = fd_ireland$FEve, FDis = fd_ireland$FDis, FD = fd_ireland$nbsp)
datatable(Functional_ireland, options= list(deferRender = TRUE, scroller = TRUE, scrollX = T, scrollY = "350px", autoWidth = T), rownames = T)
hist(Functional_ireland$FRic)
hist(Functional_ireland$raoq)
#Silwood
COMPOSICAO_SILWOOD <- COMPOSICAO %>%
filter(site == "Silwood")
#matrix de abundancia silwood
abundance_silwood <- COMPOSICAO_SILWOOD %>%
ungroup() %>%
mutate(unique_plot = paste(plot, block, year, site, sep = "_")) %>%
group_by(unique_plot) %>%
summarise(across(where(is.numeric), sum, na.rm = TRUE)) %>%
column_to_rownames(var = "unique_plot") %>%
dplyr::select(-year,-location, -sr, -fr,-fgc, -mix.nest, -mix, -biomass)
# Cria a coluna 'riqueza' contando o número de espécies em cada plot
abundance_silwood$riqueza <- rowSums(abundance_silwood > 0)
abundance_silwood_filtrado <- abundance_silwood[abundance_silwood$riqueza > 3, ]
abundance_silwood_filtrado$riqueza <- NULL
#Remover espécies que não aparecem em nenhum plot
abundance_silwood_filtrado <- abundance_silwood_filtrado[, colSums(abundance_silwood_filtrado, na.rm = TRUE) > 0]
abundance_matrix_silwood <- as.matrix(abundance_silwood_filtrado)
#Funcional Silwood
SPECIES_silwood <- data.frame(species = colnames(abundance_matrix_silwood))
SPECIES_silwood$species <- tolower(SPECIES_silwood$species)
TRAITS$species <- tolower(TRAITS$species)
TRAITS_silwood <- SPECIES_silwood %>%
inner_join(TRAITS, by = "species")
missing_traits_species_silwood <- SPECIES_silwood %>%
anti_join(TRAITS, by = "species")
# Diversidade funcional Silwood
rownames(TRAITS_silwood) <- TRAITS_silwood$species
TRAITS_rn_silwood <- TRAITS_silwood[,-1]
TRAITS_rn_silwood$SLA <- log(TRAITS_rn_silwood$SLA)
TRAITS_rn_silwood$height <- log(TRAITS_rn_silwood$height)
TRAITS_rn_silwood <- TRAITS_rn_silwood[order(rownames(TRAITS_rn_silwood)), ]
euclid_dis_silwood <- vegdist(TRAITS_rn_silwood, "euclidean")
trait_dat_silwood <- as.matrix(euclid_dis_silwood)
colnames(abundance_matrix_silwood) <- tolower(trimws(colnames(abundance_matrix_silwood)))
abundance_matrix_silwood <- abundance_matrix_silwood[, order(colnames(abundance_matrix_silwood))]
fd_silwood <- dbFD(trait_dat_silwood, abundance_matrix_silwood)
## FRic: Dimensionality reduction was required. The last 19 PCoA axes (out of 22 in total) were removed.
## FRic: Quality of the reduced-space representation = 0.9660289
Functional_silwood <- data.frame(raoq = fd_silwood$RaoQ, FRic = fd_silwood$FRic, FEve = fd_silwood$FEve, FDis = fd_silwood$FDis, FD = fd_silwood$nbsp)
datatable(Functional_silwood, options= list(deferRender = TRUE, scroller = TRUE, scrollX = T, scrollY = "350px", autoWidth = T), rownames = T)
hist(Functional_silwood$FRic)
hist(Functional_silwood$raoq)
#Switzerland
COMPOSICAO_SWITZERLAND <- COMPOSICAO %>%
filter(site == "Switzerland")
#matrix de abundancia switzerland
abundance_switzerland <- COMPOSICAO_SWITZERLAND %>%
ungroup() %>%
mutate(unique_plot = paste(plot, block, year, site, sep = "_")) %>%
group_by(unique_plot) %>%
summarise(across(where(is.numeric), sum, na.rm = TRUE)) %>%
column_to_rownames(var = "unique_plot") %>%
dplyr::select(-year,-location, -sr, -fr,-fgc, -mix.nest, -mix, -biomass)
# Cria a coluna 'riqueza' contando o número de espécies em cada plot
abundance_switzerland$riqueza <- rowSums(abundance_switzerland > 0)
abundance_switzerland_filtrado <- abundance_switzerland[abundance_switzerland$riqueza > 3, ]
abundance_switzerland_filtrado$riqueza <- NULL
#Remover espécies que não aparecem em nenhum plot
abundance_switzerland_filtrado <- abundance_switzerland_filtrado[, colSums(abundance_switzerland_filtrado, na.rm = TRUE) > 0]
abundance_matrix_switzerland <- as.matrix(abundance_switzerland_filtrado)
#Funcional Switzerland
SPECIES_switzerland <- data.frame(species = colnames(abundance_matrix_switzerland))
SPECIES_switzerland$species <- tolower(SPECIES_switzerland$species)
TRAITS$species <- tolower(TRAITS$species)
TRAITS_switzerland <- SPECIES_switzerland %>%
inner_join(TRAITS, by = "species")
missing_traits_species_switzerland <- SPECIES_switzerland %>%
anti_join(TRAITS, by = "species")
# Diversidade funcional Switzerland
rownames(TRAITS_switzerland) <- TRAITS_switzerland$species
TRAITS_rn_switzerland <- TRAITS_switzerland[,-1]
TRAITS_rn_switzerland$SLA <- log(TRAITS_rn_switzerland$SLA)
TRAITS_rn_switzerland$height <- log(TRAITS_rn_switzerland$height)
TRAITS_rn_switzerland <- TRAITS_rn_switzerland[order(rownames(TRAITS_rn_switzerland)), ]
euclid_dis_switzerland <- vegdist(TRAITS_rn_switzerland, "euclidean")
trait_dat_switzerland <- as.matrix(euclid_dis_switzerland)
colnames(abundance_matrix_switzerland) <- tolower(trimws(colnames(abundance_matrix_switzerland)))
abundance_matrix_switzerland <- abundance_matrix_switzerland[, order(colnames(abundance_matrix_switzerland))]
fd_switzerland <- dbFD(trait_dat_switzerland, abundance_matrix_switzerland)
## FRic: Dimensionality reduction was required. The last 29 PCoA axes (out of 32 in total) were removed.
## FRic: Quality of the reduced-space representation = 0.9723384
Functional_switzerland <- data.frame(raoq = fd_switzerland$RaoQ, FRic = fd_switzerland$FRic, FEve = fd_switzerland$FEve, FDis = fd_switzerland$FDis, FD = fd_switzerland$nbsp)
datatable(Functional_switzerland, options= list(deferRender = TRUE, scroller = TRUE, scrollX = T, scrollY = "350px", autoWidth = T), rownames = T)
hist(Functional_switzerland$FRic)
hist(Functional_switzerland$raoq)
FUNCIONAL juntar
Abundance_juntar
abundance_filtrado <- bind_rows(
abundance_germany_filtrado,
abundance_greece_filtrado,
abundance_ireland_filtrado,
abundance_silwood_filtrado,
abundance_switzerland_filtrado
)
multi <- cbind(dataset_biodep1, getStdAndMeanFunctions(dataset_biodep1, vars))
dataset_final <- multi %>%
left_join(Functional, by = c("plot", "site"))
datatable(dataset_final, options= list(deferRender = TRUE, scroller = TRUE, scrollY = "350px", scrollX=T, autoWidth=T), rownames=T)
cor_matrix
## raoq FRic FEve FDis FD
## raoq 1.0000000 0.42836241 0.20097360 0.9556472 0.2919102
## FRic 0.4283624 1.00000000 -0.06933825 0.3621837 0.7967759
## FEve 0.2009736 -0.06933825 1.00000000 0.2808698 -0.2216051
## FDis 0.9556472 0.36218372 0.28086977 1.0000000 0.2787411
## FD 0.2919102 0.79677593 -0.22160514 0.2787411 1.0000000
ggcorrplot(cor_matrix,
method = "circle",
type = "lower",
lab = TRUE,
title = "Índices de Diversidade Funcional")
mod1 <- glmmTMB(meanFunction ~ raoq + FRic + (1|site),data = biodepth[-46,])
summary(mod1)
## Family: gaussian ( identity )
## Formula: meanFunction ~ raoq + FRic + (1 | site)
## Data: biodepth[-46, ]
##
## AIC BIC logLik deviance df.resid
## -209.7 -196.0 109.8 -219.7 108
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.004622 0.06798
## Residual 0.007447 0.08629
## Number of obs: 113, groups: site, 5
##
## Dispersion estimate for gaussian family (sigma^2): 0.00745
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.3876263 0.0335883 11.541 < 2e-16 ***
## raoq -0.0032736 0.0012854 -2.547 0.010870 *
## FRic 0.0007642 0.0002017 3.789 0.000151 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mod_resid <- simulateResiduals(fittedModel = mod1, plot = FALSE)
plot(mod_resid) # resíduos não tá normal
outliers(mod_resid)
## integer(0)
ggplot(biodepth, aes(x = raoq, y = meanFunction)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "RAOQ",
x = "raoq", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
ggplot(biodepth, aes(x = raoq, y = meanFunction, color = as.factor(site))) +
geom_point() +
geom_smooth(aes(group = as.factor(site)), method = "lm", se = FALSE) +
labs(title = "RAOQ",
x = "raoq", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
ggplot(biodepth, aes(x = FRic, y = meanFunction)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "FRic",
x = "FRic", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
ggplot(biodepth, aes(x = FRic, y = meanFunction, color = as.factor(site))) +
geom_point() +
geom_smooth(aes(group = as.factor(site)), method = "lm", se = FALSE) +
labs(title = "FRic",
x = "FRic", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
Avaliar o efeito de diferentes níveis de fertilização com nitrogênio em um experimento de competição de árvores.
9 TRTAMENTOS de adição de nitrogênio:
1: 0g/m²/ano nitrogênio (com micronutrientes)
2: 3g/m²/ano nitrogênio (com micronutrientes)
3: 6g/m²/ano nitrogênio (com micronutrientes)
4: 10g/m²/ano nitrogênio (com micronutrientes)
5: 16g/m²/ano nitrogênio (com micronutrientes)
6: 28g/m²/ano nitrogênio (com micronutrientes)
7: 50g/m²/ano nitrogênio (com micronutrientes)
8: 80g/m²/ano nitrogênio (com micronutrientes)
9: 0g/m²/ano (sem nitrogênio sem micronitrientes)
biomassa <- read.table("e097/e097_Plant aboveground biomass data.txt", h=T, sep="\t")
Filtrando o ano 1982 porque as funções foram coletadas aqui.
biomassa <- biomassa %>%
filter(year=="1982")
Biomassa relativa
biomassa <- biomassa %>%
group_by(Field, plot.number) %>%
mutate(percentage_biomass = 100 * Species.Biomass..g.m2. / sum(Species.Biomass..g.m2., na.rm = TRUE))
datatable(biomassa, options= list(deferRender = TRUE, scrollY = "350px", scrollX=T, scroller = TRUE, autoWidth=T), rownames=T)
ggplot(biomassa, aes(x = percentage_biomass)) +
geom_histogram(binwidth = 10, fill = "lightblue", color = "black") +
labs(title = "Biomassa relativa CEDAR CREEK e97",
x = "Biomassa relativa",
y = "Frequência") +
theme_minimal()
###FUNCIONAL
# Cálculo da diversidade funcional
fd <- dbFD(trait_dat, abundance_matrix_clean)
## FRic: Dimensionality reduction was required. The last 60 PCoA axes (out of 64 in total) were removed.
## FRic: Quality of the reduced-space representation = 0.9789017
Functional <- data.frame(raoq = fd$RaoQ, FRic = fd$FRic,FEve = fd$FEve, FDis = fd$FDis, FD = fd$nbsp )
datatable(Functional, options= list(deferRender = TRUE, scrollY = "350px", scrollX=T, scroller = TRUE, autoWidth=T), rownames=T)
#Dataset junto das funções
funcoes <- potassioFunc %>%
full_join(fosforoFunc, by = c("Plot.number", "Field.letter")) %>%
full_join(calcioFunc, by = c("Plot.number", "Field.letter")) %>%
full_join(magnesioFunc, by = c("Plot.number", "Field.letter")) %>%
full_join(nitroFunc, by = c("Plot.number", "Field.letter")) %>%
full_join(phFunc, by = c("Plot.number", "Field.letter"))
e97_multifuncionalidade <- cbind(datset_97, getStdAndMeanFunctions(datset_97, vars))
dataset_final_e97 <- e97_multifuncionalidade %>%
left_join(Functional, by = c("Plot.number", "treatment", "Field.letter"))
datatable(dataset_final_e97, options= list(deferRender = TRUE, scrollY = "350px", scrollX=T, scroller = TRUE, autoWidth=T), rownames=T)
cor_matrix
## raoq FRic FEve FDis FD
## raoq 1.0000000 0.1762663 0.19790931 0.9697383 -0.19715587
## FRic 0.1762663 1.0000000 0.15502712 0.1612947 0.79029689
## FEve 0.1979093 0.1550271 1.00000000 0.3268466 0.04754828
## FDis 0.9697383 0.1612947 0.32684660 1.0000000 -0.18431343
## FD -0.1971559 0.7902969 0.04754828 -0.1843134 1.00000000
ggcorrplot(cor_matrix,
method = "circle",
type = "lower",
lab = TRUE,
title = "Índices de Diversidade Funcional")
mod1 <- glmmTMB(log(meanFunction) ~ raoq + FRic + (1|Field.letter),data = e97)
summary(mod1)
## Family: gaussian ( identity )
## Formula: log(meanFunction) ~ raoq + FRic + (1 | Field.letter)
## Data: e97
##
## AIC BIC logLik deviance df.resid
## -247.4 -234.0 128.7 -257.4 103
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Field.letter (Intercept) 0.008875 0.09421
## Residual 0.004963 0.07045
## Number of obs: 108, groups: Field.letter, 2
##
## Dispersion estimate for gaussian family (sigma^2): 0.00496
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.713e-01 8.333e-02 -4.456 8.36e-06 ***
## raoq -1.744e-03 8.262e-04 -2.111 0.0348 *
## FRic -6.280e-06 5.547e-05 -0.113 0.9099
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mod_resid <- simulateResiduals(fittedModel = mod1, plot = FALSE)
plot(mod_resid) # resíduos não tá normal
outliers(mod_resid)
## [1] 26
ggplot(e97, aes(x = raoq, y = meanFunction)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "RAOQ",
x = "raoq", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
ggplot(e97, aes(x = raoq, y = meanFunction, color = as.factor(Field.letter))) +
geom_point() +
geom_smooth(aes(group = as.factor(Field.letter)), method = "lm", se = FALSE) +
labs(title = "RAOQ",
x = "raoq", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
#Tirando outliers
e97_semot <- e97 %>%
filter(!raoq <40) %>%
filter(!meanFunction >0.9)
mod1 <- glmmTMB(meanFunction ~ raoq + FRic + (1|Field.letter),data = e97_semot)
summary(mod1)
## Family: gaussian ( identity )
## Formula: meanFunction ~ raoq + FRic + (1 | Field.letter)
## Data: e97_semot
##
## AIC BIC logLik deviance df.resid
## -365.3 -352.0 187.6 -375.3 101
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Field.letter (Intercept) 0.002786 0.05278
## Residual 0.001558 0.03947
## Number of obs: 106, groups: Field.letter, 2
##
## Dispersion estimate for gaussian family (sigma^2): 0.00156
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 6.475e-01 4.952e-02 13.076 <2e-16 ***
## raoq -5.073e-04 5.304e-04 -0.956 0.339
## FRic 1.815e-05 3.150e-05 0.576 0.564
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ggplot(e97_semot, aes(x = raoq, y = meanFunction)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "RAOQ",
x = "raoq", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
ggplot(e97_semot, aes(x = raoq, y = meanFunction, color = as.factor(Field.letter))) +
geom_point() +
geom_smooth(aes(group = as.factor(Field.letter)), method = "lm", se = FALSE) +
labs(title = "RAOQ",
x = "raoq", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
ggplot(e97_semot, aes(x = FRic, y = meanFunction)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "FRic",
x = "FRic", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
Biodiversity II, effects od plant biodiversity on population and ecossystem process
168 parcelas de 9m x 9m.
Tratamentos: Parcelas com 1, 2, 4, 8 ou 16 espécies de plantas, com cerca de 30 réplicas para cada nível de diversidade.
datatable(cover, options= list(deferRender = TRUE, scrollY = "350px", scrollX=T, scroller = TRUE, autoWidth=T), rownames=T)
# Calcular a cobertura relativa por quadrante dentro de cada plot
cover <- cover %>%
group_by(Year, Plot, Quadrat) %>%
mutate(percentage_cover = 100 * Abscover / sum(Abscover, na.rm = TRUE))
# Somar a cobertura por espécie dentro de cada plot e ano
cover <- cover %>%
group_by(Year, Plot, Species) %>%
summarize(Total_Abscover = sum(percentage_cover, na.rm = TRUE)) %>%
ungroup()
## `summarise()` has grouped output by 'Year', 'Plot'. You can override using the
## `.groups` argument.
# Padronizar para que a soma total seja 100% dentro de cada plot
cover <- cover %>%
group_by(Year, Plot) %>%
mutate(cover_final = 100 * Total_Abscover / sum(Total_Abscover, na.rm = TRUE)) %>%
ungroup()
ggplot(cover, aes(x = Total_Abscover)) +
geom_histogram(bins = 50, fill = "blue", color = "black")
ggplot(cover, aes(x = cover_final)) +
geom_histogram(bins = 50, fill = "blue", color = "black")
fd_96 <- dbFD(trait_dat_96, abundance_96_matrix)
## FRic: Dimensionality reduction was required. The last 65 PCoA axes (out of 67 in total) were removed.
## FRic: Quality of the reduced-space representation = 0.9626396
Functional_96 <- data.frame(raoq = fd_96$RaoQ, FRic = fd_96$FRic,FEve = fd_96$FEve, FDis = fd_96$FDis, FD = fd_96$nbsp )
hist(Functional_96$FRic)
hist(Functional_96$raoq)
SPECIES_00 <- data.frame(species = colnames(abundance_00_matrix))
SPECIES_00$Species <- tolower(SPECIES_00$species)
TRAITS$Species <- tolower(TRAITS$Species)
TRAITS_00 <- SPECIES_00 %>%
inner_join(TRAITS, by = "Species") %>%
dplyr::select(Species, Leaf.area.per.leaf.dry.mass, Plant.height.vegetative)
rownames(TRAITS_00) <- TRAITS_00$Species
TRAITS_00_rn <- TRAITS_00[,-1]
TRAITS_00_rn <- TRAITS_00_rn[order(rownames(TRAITS_00_rn)), ]
# Matriz de distância dos traits para o ano 2000
euclid_dis_00 <- vegdist(TRAITS_00_rn, "euclidean")
trait_dat_00 <- as.matrix(euclid_dis_00)
colnames(abundance_00_matrix) <- tolower(trimws(colnames(abundance_00_matrix)))
abundance_00_matrix <- abundance_00_matrix[, order(colnames(abundance_00_matrix))]
colnames(abundance_00_matrix) <- tolower(trimws(colnames(abundance_00_matrix)))
rownames(trait_dat_00) <- tolower(trimws(rownames(trait_dat_00)))
abundance_00_matrix <- abundance_00_matrix[, order(colnames(abundance_00_matrix))]
trait_dat_00 <- trait_dat_00[order(rownames(trait_dat_00)), order(colnames(trait_dat_00))]
# Cálculo da diversidade funcional para o ano 2000
fd_00 <- dbFD(trait_dat_00, abundance_00_matrix)
## FEVe: Could not be calculated for communities with <3 functionally singular species.
## FDis: Equals 0 in communities with only one functionally singular species.
## FRic: To respect s > t, FRic could not be calculated for communities with <3 functionally singular species.
## FRic: Dimensionality reduction was required. The last 61 PCoA axes (out of 64 in total) were removed.
## FRic: Quality of the reduced-space representation = 0.9764963
## FDiv: Could not be calculated for communities with <3 functionally singular species.
Functional_00 <- data.frame(raoq = fd_00$RaoQ, FRic = fd_00$FRic, FEve = fd_00$FEve, FDis = fd_00$FDis, FD = fd_00$nbsp)
# Visualização dos histogramas para o ano 2000
hist(Functional_00$FRic)
hist(Functional_00$raoq)
# Combina
Functional_120 <- bind_rows(Functional_96, Functional_00)
#quantidades de amostragem tudo diferente nos anos
#carbon_nitroFunc 1164
#biomassaFunc 1862
#soil_carbonFunc 726
#nitrat_amoniFunc 1194
#soil_nitroFunc 726
data_list <- list(carbon_nitroFunc, biomassaFunc, soil_carbonFunc, nitrat_amoniFunc, soil_nitroFunc)
combined_data <- reduce(data_list, full_join, by = c("Year", "Plot"))
#remover todos os dados ausentes
#so sobra 1996 e 2000
funcoes_120 <- na.omit(combined_data)
e120_multifuncionalidade <- cbind(dataset_120, getStdAndMeanFunctions(dataset_120, vars120))
dataset_final_120 <- e120_multifuncionalidade %>%
left_join(Functional_120, by = c("Plot", "Year"))
datatable(dataset_final_120, options= list(deferRender = TRUE, scrollY = "350px", scrollX=T, scroller = TRUE, autoWidth=T), rownames=T)
#Vou apenas cortar
##Help####
dataset_final_120 <- dataset_final_120 %>%
filter(FDis < 10, raoq < 50, FRic < 200)
cor_matrix
## raoq FRic FEve FDis FD
## raoq 1.0000000 0.3617443 0.20966865 0.9034204 0.31977113
## FRic 0.3617443 1.0000000 -0.03683210 0.2803202 0.62470882
## FEve 0.2096687 -0.0368321 1.00000000 0.1254367 0.03035598
## FDis 0.9034204 0.2803202 0.12543665 1.0000000 0.37908210
## FD 0.3197711 0.6247088 0.03035598 0.3790821 1.00000000
ggcorrplot(cor_matrix,
method = "circle",
type = "lower",
lab = TRUE,
title = "Índices de Diversidade Funcional")
mod1 <- glmmTMB(log(meanFunction) ~ raoq + FRic + (1|Year),data = e120)
summary(mod1)
## Family: gaussian ( identity )
## Formula: log(meanFunction) ~ raoq + FRic + (1 | Year)
## Data: e120
##
## AIC BIC logLik deviance df.resid
## -153.8 -136.1 81.9 -163.8 251
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Year (Intercept) 0.0001907 0.01381
## Residual 0.0307340 0.17531
## Number of obs: 256, groups: Year, 2
##
## Dispersion estimate for gaussian family (sigma^2): 0.0307
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0245998 0.0409540 -25.018 < 2e-16 ***
## raoq 0.0046731 0.0011239 4.158 3.21e-05 ***
## FRic -0.0000661 0.0003732 -0.177 0.859
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mod_resid <- simulateResiduals(fittedModel = mod1, plot = FALSE)
plot(mod_resid) # resíduos não tá normal
outliers(mod_resid)
## [1] 12 73 148 185
ggplot(e120, aes(x = raoq, y = meanFunction)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "RAOQ",
x = "raoq", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 9 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggplot(e120, aes(x = raoq, y = meanFunction, color = as.factor(Year))) +
geom_point() +
geom_smooth(aes(group = as.factor(Year)), method = "lm", se = FALSE) +
labs(title = "RAOQ",
x = "raoq", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 9 rows containing non-finite outside the scale range (`stat_smooth()`).
## Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).
#Tirando outliers
e120_semot <- e120 %>%
filter(!raoq >100) %>%
filter(!FRic >150)
mod1 <- glmmTMB(meanFunction ~ raoq + FRic + (1|Year),data = e120_semot)
summary(mod1)
## Family: gaussian ( identity )
## Formula: meanFunction ~ raoq + FRic + (1 | Year)
## Data: e120_semot
##
## AIC BIC logLik deviance df.resid
## -428.6 -412.9 219.3 -438.6 167
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Year (Intercept) 3.796e-12 1.948e-06
## Residual 4.571e-03 6.761e-02
## Number of obs: 172, groups: Year, 2
##
## Dispersion estimate for gaussian family (sigma^2): 0.00457
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.564e-01 1.065e-02 33.46 < 2e-16 ***
## raoq 2.243e-03 5.463e-04 4.11 4.02e-05 ***
## FRic 7.081e-05 1.266e-04 0.56 0.576
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ggplot(e120_semot, aes(x = raoq, y = meanFunction)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "RAOQ",
x = "raoq", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggplot(e120_semot, aes(x = raoq, y = meanFunction, color = as.factor(Year))) +
geom_point() +
geom_smooth(aes(group = as.factor(Year)), method = "lm", se = FALSE) +
labs(title = "RAOQ",
x = "raoq", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 5 rows containing non-finite outside the scale range (`stat_smooth()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggplot(e120_semot, aes(x = FRic, y = meanFunction)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "FRic",
x = "FRic", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 5 rows containing non-finite outside the scale range (`stat_smooth()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggplot(e120_semot, aes(x = FRic, y = meanFunction, color=as.factor(Year))) +
geom_point() +
geom_smooth(aes(group = as.factor(Year)), method = "lm", se = FALSE) +
labs(title = "FRic",
x = "FRic", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 5 rows containing non-finite outside the scale range (`stat_smooth()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
Tratamento 1 - Sem história de solo e sem história de plantas: O solo e a camada de plantas foram removidos até 30 cm de profundidade e substituídos por solo de um campo arável adjacente. Tratamento 2 - Com história de solo, mas sem história de plantas: A camada de plantas foi removida, mas o solo original foi mantido. Tratamento 3 - Com história de solo e de plantas: As parcelas existentes do experimento principal, estabelecidas desde 2002, foram mantidas como controle de longo prazo, mantendo tanto a história do solo quanto a das plantas. Esses tratamentos foram projetados para testar como a história do solo e das plantas influenciam a produtividade vegetal e as interações ecológicas ao longo do tempo, considerando tanto a diversidade de plantas quanto os efeitos do histórico do solo sobre a biomassa e outros fatores ecológicos.
80 plots 3 tratamentos 3 anos
cobertura_2017 <- read.csv("dbef/PLANT_COVER_2017.csv", sep = ";")
cobertura_2018 <- read.csv("dbef/PLANT_COVER_2018.csv", sep = ";")
cobertura_2019 <- read.csv("dbef/PLANT_COVER_2019.csv", sep = ";")
cobertura_total <- bind_rows(cobertura_2017, cobertura_2018, cobertura_2019)
datatable(cobertura_total, options= list(deferRender = TRUE, scrollY = "350px", scrollX=T, scroller = TRUE, autoWidth=T), rownames=T)
## Warning in instance$preRenderHook(instance): It seems your data is too big for
## client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html
#diversidade funcional
fd_dbef <- dbFD(trait_dat_dbef, abundance_dbef_matrix_clean)
## FEVe: Could not be calculated for communities with <3 functionally singular species.
## FDis: Equals 0 in communities with only one functionally singular species.
## FRic: To respect s > t, FRic could not be calculated for communities with <3 functionally singular species.
## FRic: Dimensionality reduction was required. The last 57 PCoA axes (out of 59 in total) were removed.
## FRic: Quality of the reduced-space representation = 0.7574923
## FDiv: Could not be calculated for communities with <3 functionally singular species.
Functional_dbef <- data.frame(raoq = fd_dbef$RaoQ, FRic = fd_dbef$FRic,FEve = fd_dbef$FEve, FDis = fd_dbef$FDis, FD = fd_dbef$nbsp )
datatable(Functional_dbef, options= list(deferRender = TRUE, scrollY = "350px", scrollX=T, scroller = TRUE, autoWidth=T), rownames=T)
# Combinar os Dados com a Base
dados_combinados <- base %>%
left_join(cobertura_media, by = c("plot", "treatment", "year")) %>%
left_join(biomass_summary, by = c("plot", "treatment", "year")) %>%
left_join(atv_micro, by = c("plot", "treatment", "year")) %>%
left_join(herbivoria_total, by = c("plot", "treatment", "year")) %>%
left_join(predacao, by = c("plot", "treatment", "year"))
dbef_multifunc <- cbind(dados_combinados, getStdAndMeanFunctions(dados_combinados, func_vars))
dataset_final_JENA <- dbef_multifunc %>%
left_join(Functional_dbef, by = c("plot", "treatment", "year"))
datatable(dataset_final_JENA, options= list(deferRender = TRUE, scrollY = "350px", scrollX=T, scroller = TRUE, autoWidth=T), rownames=T)
cor_matrix
## raoq FRic FEve FDis FD
## raoq 1.0000000 0.55127018 0.33838727 0.9545245 0.4651809
## FRic 0.5512702 1.00000000 0.09063385 0.4986894 0.6718329
## FEve 0.3383873 0.09063385 1.00000000 0.3517603 0.2450418
## FDis 0.9545245 0.49868937 0.35176030 1.0000000 0.4626148
## FD 0.4651809 0.67183287 0.24504183 0.4626148 1.0000000
ggcorrplot(cor_matrix,
method = "circle",
type = "lower",
lab = TRUE,
title = "Índices de Diversidade Funcional")
mod1 <- glmmTMB(meanFunction ~ raoq + FRic + (1|year),data = jena)
summary(mod1)
## Family: gaussian ( identity )
## Formula: meanFunction ~ raoq + FRic + (1 | year)
## Data: jena
##
## AIC BIC logLik deviance df.resid
## -1285.8 -1266.2 647.9 -1295.8 364
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## year (Intercept) 0.0008237 0.02870
## Residual 0.0016904 0.04111
## Number of obs: 369, groups: year, 3
##
## Dispersion estimate for gaussian family (sigma^2): 0.00169
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.125e-01 1.723e-02 12.334 < 2e-16 ***
## raoq -2.653e-04 1.732e-04 -1.532 0.125645
## FRic 1.402e-04 4.223e-05 3.319 0.000903 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mod_resid <- simulateResiduals(fittedModel = mod1, plot = FALSE)
plot(mod_resid) # resíduos não tá normal
outliers(mod_resid)
## [1] 363
ggplot(jena, aes(x = raoq, y = meanFunction)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "RAOQ",
x = "raoq", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 164 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 164 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggplot(jena, aes(x = raoq, y = meanFunction, color = as.factor(year))) +
geom_point() +
geom_smooth(aes(group = as.factor(year)), method = "lm", se = FALSE) +
labs(title = "RAOQ",
x = "raoq", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 164 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 164 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggplot(jena, aes(x = raoq, y = meanFunction, color = as.factor(year))) +
geom_point() +
geom_smooth(aes(group = as.factor(year)), method = "lm", se = FALSE) +
labs(title = "RAOQ",
x = "raoq", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 164 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 164 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggplot(jena, aes(x = FRic, y = meanFunction, color = as.factor(year))) +
geom_point() +
geom_smooth(aes(group = as.factor(year)), method = "lm", se = FALSE) +
labs(title = "FRic",
x = "FRic", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 351 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 351 rows containing missing values or values outside the scale range
## (`geom_point()`).
#Tirando outliers
jena_semot <- jena %>%
filter(!raoq >75)
mod1 <- glmmTMB(meanFunction ~ raoq + FRic + (1|year),data = jena_semot)
summary(mod1)
## Family: gaussian ( identity )
## Formula: meanFunction ~ raoq + FRic + (1 | year)
## Data: jena_semot
##
## AIC BIC logLik deviance df.resid
## -1281.6 -1262.0 645.8 -1291.6 363
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## year (Intercept) 0.0008164 0.02857
## Residual 0.0016935 0.04115
## Number of obs: 368, groups: year, 3
##
## Dispersion estimate for gaussian family (sigma^2): 0.00169
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.118e-01 1.720e-02 12.311 < 2e-16 ***
## raoq -2.293e-04 1.840e-04 -1.247 0.21247
## FRic 1.375e-04 4.253e-05 3.232 0.00123 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ggplot(jena_semot, aes(x = raoq, y = meanFunction)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "RAOQ",
x = "raoq", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 160 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 160 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggplot(jena_semot, aes(x = raoq, y = meanFunction, color = as.factor(year))) +
geom_point() +
geom_smooth(aes(group = as.factor(year)), method = "lm", se = FALSE) +
labs(title = "RAOQ",
x = "raoq", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 160 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 160 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggplot(jena_semot, aes(x = FRic, y = meanFunction)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "FRic",
x = "FRic", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 347 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 347 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggplot(jena_semot, aes(x = FRic, y = meanFunction, color=as.factor(year))) +
geom_point() +
geom_smooth(aes(group = as.factor(year)), method = "lm", se = FALSE) +
labs(title = "FRic",
x = "FRic", y = "meanFunction") +
theme_get()
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 347 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 347 rows containing missing values or values outside the scale range
## (`geom_point()`).